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import pandas as pd
import numpy as np
import plotly.express as px
import seaborn as sns
import matplotlib.pyplot as plt
from fbprophet import Prophet
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dataset_url="https://raw.githubusercontent.com/datasets/covid-19/master/data/countries-aggregated.csv"
df=pd.read_csv(dataset_url)
df = df[df['Confirmed']>0]
df.head()
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fig=px.choropleth(df,locations='Country',locationmode='country names',color='Confirmed'
,animation_frame='Date')
fig.update_layout(title_text='Global spread of COVID-19')
fig.show()
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fig = px.choropleth(df,locations="Country",locationmode='country names',color='Deaths',animation_frame='Date')
fig.update_layout(title_text='Global Deaths of COVID-19')
fig.show()
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fig = px.choropleth(df,locations="Country",locationmode='country names',color='Recovered',animation_frame='Date')
fig.update_layout(title_text='Global Recovered of COVID-19')
fig.show()
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def visualizer(df,lockdown_start_date,lockdown_end_date,cases):
print("VISUALIZER")
print()
print(df.head())
print()
print(df.tail())
st = ""
if cases == "Confirmed":
st = "Infection Rate"
if cases == "Deaths":
st = "Death Rate"
if cases == "Recovered":
st = "Recovered Rate"
df[st] = df[cases].diff()
df = df[df[st]>0]
fig=px.line(df,x="Date",y=st)
fig.add_shape(dict(type="line",x0=lockdown_start_date,y0=0,x1=lockdown_start_date,y1=df[st].max(),line=dict(color="red",width=2)))
fig.add_annotation(dict(x=lockdown_start_date,y=df[st].max(),text='starting date of the lockdown'))
fig.add_shape(dict(type='line',x0=lockdown_end_date,y0=0,x1=lockdown_end_date,y1=df[st].max(),line=dict(color="red",width=2)))
fig.add_annotation(dict(x=lockdown_end_date,y=df[st].max(),text="lockdown end date"))
fig.show()
print("Maximum "+st+" till now is ",df[st].max())
df = df.dropna()
df = df.drop(st,axis=1)
df.groupby("Country").sum().plot.barh()
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def predictor(df,cases):
print("PREDICTOR")
df = df.sort_values('Date')
df = df[df[cases]>0]
prophet_df = df[['Date',cases]]
print(prophet_df.head())
prophet_df = prophet_df.rename(columns={'Date':'ds',cases:'y'})
m = Prophet()
m.fit(prophet_df)
future = m.make_future_dataframe(periods=365)
forecast = m.predict(future)
m.plot(forecast,xlabel='Date',ylabel=cases)
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input_df = input("Enter country name")
lsd = input("lockdown start date")
led = input("lockdown end date")
cases = input("Enter case(Confirmed,Deaths or Recovered)")
visualizer(df[df["Country"]==input_df],lsd,led,cases)
predictor(df[df["Country"]==input_df],cases)
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